Navigating the maze of programming languages can be overwhelming, especially if you are new to data science and analytics. Two competing languages are Julia and R. In this article, we compare the main elements of Julia vs R programming languages that set them apart from one another and the current job market for each of these skills.
Overview of Julia and R
The R programming language is a specialized statistical software that has been around for almost 30 years. It is a mature programming language with packages that allow you to do almost anything you want. However, it is predominantly used for statistics applications, including data science and nearly all forms of data analysis and data manipulation. We have an Introduction to R course that will quickly get you up to speed on the basics.
For more general-purpose programming, one would turn to other programming languages instead of R. One of these languages is Julia, a much newer programming language having only been around for about 10 years. Julia specializes in all forms of scientific programming but also allows you to do more general-purpose programming. The stand-out feature of Julia is its speed. It can handle large amounts of data and complex computations at blazing speeds while still being easy to use. For an introduction to Julia, check out our course.
Choosing a Programming Language
When joining a data-related field like data science, data analytics, or statistics, you must choose a programming language to learn. This decision is based on many factors, such as the industry you want to join and the nature of your job. You can read about the top programming languages for data scientists in a separate article.
However, a crucial part of choosing a programming language is to get started. There is no perfect programming language to solve every kind of problem. Some languages are better or more efficient than others in solving particular problems. Therefore, you will likely need to learn more than one programming language and other tools throughout your career in data.
It is also essential to focus on just one programming language at a time to master it faster than if you tried to learn several languages at once.
An excellent programming language to learn first is R. However, Julia is becoming a solid contender that any new or experienced programmer or analyst should consider. We have a separate blog post on the rise of Julia and whether it is worth learning.
Julia vs R Comparison
Both Julia and R are specialized programming languages designed to analyze and manipulate data. This makes them good choices for data scientists, data analysts, and statisticians.
In this section, we will go over the four main elements of these programming languages that sets them apart from one another:
- IDEs and Notebooks
- Resources and Support
IDEs and Notebooks
An Interactive Development Environment (IDE) is an application for writing, testing, and debugging code, often within a single graphical user interface. They offer many features that help you to develop new applications and solutions quicker.
The most common IDE is Visual Studio Code, or VSCode for short. You can add community-built extensions to VSCode that support nearly every programming language, including Julia and R. However, VSCode is the most popular IDE among Julia users with the Julia for VSCode extension.
R-Studio is an IDE that has become synonymous with the R programming language. You will be hard-pressed to find an R programmer that does not use or has at least tried, R-Studio as their IDE. R-Studio also supports other programming languages, like Julia and Python. Check out our full R-Studio guide for more information.
In terms of notebooks, you can use Jupyter Notebooks for both Julia and R. The name Jupyter actually stands for Julia, Python, and R. You can check out our Jupyter cheat sheet to find out more about the notebook app.
While Jupyter has become commonplace in data science, it comes with some downsides, particularly with version control and reactivity. This is where R and Julia can use more specialized notebook software that often overcome these downsides.
The R-Studio IDE offers the R Markdown file format, which lets you write code in notebooks. Julia, however, has specialized notebook software in the form of a package called Pluto. Pluto notebooks are reactive and interactive, which ensures code cells are always up-to-date and allows you to add interactive elements like sliders and input boxes.
Julia and R have similar syntax because both are dynamically typed, interpreted programming languages with the same control structures like loops and conditional logic. You can use these cheat sheets to quickly and easily reference the syntax of these languages:
However, there are two main aspects of the syntax that you must consider when comparing R and Julia;
- Single vs multiple dispatch
R uses an object-oriented programming approach through two systems called S3 and S4. The original implementation of R uses single dispatch in the S3 system to identify the most appropriate method based on the first argument's class to a function. However, the new S4 system uses multiple dispatch, where types are matched when choosing the most appropriate method to call.
Multiple dispatch is built into the design of Julia, and the implementation of it is very intuitive and incredibly fast.
In addition, there is no need to write vectorized code in Julia since it is already incredibly fast without it. However, vectorized code is a requirement with R to improve speed and performance.
There are just under 19,000 packages on the CRAN repository that houses all the available R packages. That is an impressive number of packages! Since R has been around for a few decades, there has been more than enough time for R users and experts to build and submit their own packages. This makes R an extremely versatile programming language, capable of providing solutions to many problems.
On the other hand, there are over 7,400 packages registered on Julia's general registry. Since Julia is a relatively young programming language, it still needs quite a bit more time to catch up to a mature language like R.
Both R and Julia allow you to call functions between each other and from other programming languages like Python. A good example of this is the popular deep learning Python package Keras, which can be called from both R and Julia. However, a downside is that you lose out on some of the benefits of the programming language you're calling it from. For example, if you call a Python package from within Julia, you would lose out on Julia's incredible speed and multiple dispatch.
In addition, both R and Julia handle package management very well. R has dedicated libraries for package and dependency management - Packrat and renv. Both allow you to create isolated project environments and keep track of package dependencies.
Julia has a built-in package manager called Pkg, which handles package installation, updates, and removal. Pkg allows packages to be managed within the local environment you are working in. Everything is stored in a local manifest file that can also be version controlled. However, the truly great thing about package management with Pkg in Julia is that environments are stackable. This means you can use the packages and their dependencies from another environment within the one you're working in.
Resources and Support
Due to R's popularity and widespread adoption, there are far more resources and community support than Julia. As an example, we compare the number of current (at the time of writing) active questions on Stack Overflow between Julia and R -- this is around 470,000 for R and only around 11,000 for Julia. This demonstrates the big difference in the number of users of each programming language.
However, there are now many resources available about Julia, with more added constantly as the programming language grows in its user base and popularity. The Julia community is also very active and welcoming to new users. This is demonstrated in the percentage of currently unanswered questions on Stack Overflow between Julia and R -- this is around 26% for R and only 15% for Julia.
R vs Julia Comparison Table
We’ve compiled the information about the two languages into a table to help you understand the key similarities and differences:
IDEs and Notebooks
VSCode, Pluto notebooks (reactive and interactive)
R-Studio, R Markdown notebooks
Multiple dispatch, no need for vectorization
Single dispatch, requires vectorization
Over 7,400 packages on registry
Almost 19,000 packages on CRAN repository
Resources and Support
Strong online community and documentation
Strong online community and extensive history
Large number of packages, versatility
Interoperability with other languages
Can call functions from other languages, including R and Python
Can call functions from other languages, including Julia and Python
This table provides a high-level overview of the main differences between Julia and R. It is worth noting that this is not an exhaustive list of all the differences between the two languages and that there may be other factors to consider when choosing between Julia and R for a specific project.
Job Market for Julia and R
R is far more widely adopted in academia and business than Julia, so it is much easier to find a job if you can demonstrate proficiency in using the R programming language.
While fewer businesses use Julia in their data science and analytics projects, the Julia programming language is starting to grow in popularity. We have a blog post exploring the applications of Julia programming. Check it out if you want to find out what Julia is used for today. Businesses are now beginning to recognize the enormous benefit of using Julia to develop solutions to their business problems. This is especially evident in the potential cost saving of running Julia code in production due to Julia's impressive speed and performance.
To get an idea of the job outlook for each of these skills, we compare the number of job openings (according to LinkedIn) in the US for R and Julia where they are listed as skill requirements. There are currently over 150,000 open positions for R and only 1,190 open positions for Julia.
Data science and analytics are evolving fields where you must use and develop solutions with more than one tool or programming language. Therefore, when just starting your career in data, you will need to choose a programming language to learn first - this could be R, Julia, Python, MATLAB, SAS, or even C.
However, the most important thing is picking one and getting started. Eventually, you will need to expand your skillset, and it is highly recommended that you learn a second programming language. You may also pick up skills in a variety of different tools along the way - such as data visualization tools like Power BI and Tableau. You can also learn data visualization with R with DataCamp's track.
Based on the popularity of R and the volume of job openings in business today, R would be an excellent first choice for a beginner in data science and related fields. However, if you have already been working in a data-related role for a few years, you might find immense value in branching out into learning Julia as it can bring significant benefits to your current or future employer.
Julia vs R FAQs
Is Julia faster than R?
Julia is generally known for its fast performance, particularly when it comes to numerical and scientific computing. R is not as fast as Julia, but it has a large number of packages and is versatile, which may make it a better choice for certain projects.
Can I use R and Julia together in a project?
Yes, it is possible to call functions between R and Julia, as well as from other programming languages like Python. This can be useful if you want to take advantage of the strengths of both languages in a single project.
Can I use the same tools and resources for both R and Julia?
Both R and Julia can be used with Jupyter Notebooks and have strong online communities and documentation. However, each language also has its own specific tools and resources, such as R-Studio for R and Pluto notebooks for Julia.
Julia and R Courses
40 R Programming Interview Questions & Answers For All Levels
Navigating R Certifications in 2024: A Comprehensive Guide
K-Nearest Neighbors (KNN) Classification with R Tutorial
Introduction to Non-Linear Models and Insights Using R
Visualizing Climate Change Data with ggplot2: A Step-by-Step Tutorial